FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916383266177024 |
|---|---|
| author | Feng, Xiang Wang, Chengkai Wu, Chengyu Li, Yunxiang He, Yongbo Wang, Shuai Wang, Yaiqi |
| author_facet | Feng, Xiang Wang, Chengkai Wu, Chengyu Li, Yunxiang He, Yongbo Wang, Shuai Wang, Yaiqi |
| contents | Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to elevate the quality of segmentation performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06551 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image Feng, Xiang Wang, Chengkai Wu, Chengyu Li, Yunxiang He, Yongbo Wang, Shuai Wang, Yaiqi Computer Vision and Pattern Recognition Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to elevate the quality of segmentation performance. |
| title | FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.06551 |